AIMC Topic: Water Quality

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Predicting the concentration of total coliforms in treated rural domestic wastewater by multi-soil-layering (MSL) technology using artificial neural networks.

Ecotoxicology and environmental safety
Many indicators are involved in monitoring water quality. For instance, the fecal indicator bacteria are extremely important to detect the water quality. For this purpose, to better predict the total coliforms at the outlet of a Multi-Soil-Layering (...

Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index.

Environmental science and pollution research international
In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inferen...

Regional Water Resources Security Evaluation Based on a Hybrid Fuzzy BWM-TOPSIS Method.

International journal of environmental research and public health
Nowadays, water resource security is becoming increasingly prominent, and this problem is a primary bottleneck restricting China's future sustainable development. It is difficult to come to a unified conclusion on water resources security, and applic...

Assessing the biochemical oxygen demand using neural networks and ensemble tree approaches in South Korea.

Journal of environmental management
The biochemical oxygen demand (BOD), one of widely utilized variables for water quality assessment, is metric for the ecological division in rivers. Since the traditional approach to predict BOD is time-consuming and inaccurate due to inconstancies i...

A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning.

Water research
Predicting recreational water quality is one of the most difficult tasks in water management with major implications for humans and society. Many data-driven models have been used to predict water quality indicators to allow a real time assessment of...

A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning.

Sensors (Basel, Switzerland)
Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this informat...

A water quality prediction method based on the multi-time scale bidirectional long short-term memory network.

Environmental science and pollution research international
As an important factor affecting the mangrove wetland ecosystem, water quality has become the focus of attention in recent years. Therefore, many studies have focused on the prediction of water quality to help establish a regulatory framework for the...

Hybrid decision tree-based machine learning models for short-term water quality prediction.

Chemosphere
Water resources are the foundation of people's life and economic development, and are closely related to health and the environment. Accurate prediction of water quality is the key to improving water management and pollution control. In this paper, t...

Contamination source identification in water distribution networks using convolutional neural network.

Environmental science and pollution research international
Contamination source identification (CSI) is significant for water quality security and social stability when a contamination intrusion event occurs in water distribution systems (WDSs). However, in research, this is an extremely challenging task for...

Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes.

Environmental science and pollution research international
Chlorophyll-a (CHLA) is a key indicator to represent eutrophication status in lakes. In this study, CHLA, total phosphorus (TP), total nitrogen (TN), turbidity (TB), and Secchi depth (SD) collected by the United States Environmental Protection Agency...